{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d037743",
   "metadata": {
    "id": "5d037743"
   },
   "source": [
    "# Exploring PTT-PPG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbae8e9b",
   "metadata": {
    "id": "fbae8e9b"
   },
   "source": [
    "Let's begin by exploring data in the Pulse Transit Time PPG dataset.\n",
    "\n",
    "Our **objectives** are to:\n",
    "- Understand what records are present in the dataset.\n",
    "- Find out which signals are present in a record, and how long the signals last.\n",
    "- Load waveforms using the WFDB toolbox.\n",
    "- Plot the waveforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b240726",
   "metadata": {
    "id": "0b240726"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><b>Resource:</b> You can find out more about the Pulse Transit Time PPG dataset <a href=\"https://physionet.org/content/pulse-transit-time-ppg/1.1.0/\">here</a>.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b8e213",
   "metadata": {
    "id": "28b8e213"
   },
   "source": [
    "---\n",
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dac032e",
   "metadata": {
    "id": "5dac032e"
   },
   "source": [
    "### Specify the required Python packages\n",
    "We'll import the following:\n",
    "- _sys_: an essential python package\n",
    "- _pathlib_ (well a particular function from _pathlib_, called _Path_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce3cdfde",
   "metadata": {
    "id": "ce3cdfde"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9976c5e4",
   "metadata": {
    "id": "9976c5e4"
   },
   "source": [
    "### Specify a particular version of the WFDB Toolbox"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6533154b",
   "metadata": {
    "id": "6533154b"
   },
   "source": [
    "- _wfdb_: For this workshop we will be using version 4 of the WaveForm DataBase (WFDB) Toolbox package. The package contains tools for processing waveform data such as those found in this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5fdfa989",
   "metadata": {
    "id": "5fdfa989"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wfdb==4.1.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (4.1.0)\n",
      "Requirement already satisfied: pandas<2.0.0,>=1.0.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb==4.1.0) (1.2.4)\n",
      "Requirement already satisfied: requests<3.0.0,>=2.8.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb==4.1.0) (2.25.1)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.10.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb==4.1.0) (1.20.1)\n",
      "Requirement already satisfied: matplotlib<4.0.0,>=3.2.2 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb==4.1.0) (3.3.4)\n",
      "Requirement already satisfied: scipy<2.0.0,>=1.0.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb==4.1.0) (1.6.2)\n",
      "Requirement already satisfied: SoundFile<0.12.0,>=0.10.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from wfdb==4.1.0) (0.10.3.post1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.1.0) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.1.0) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.1.0) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.1.0) (8.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from matplotlib<4.0.0,>=3.2.2->wfdb==4.1.0) (1.3.1)\n",
      "Requirement already satisfied: six in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from cycler>=0.10->matplotlib<4.0.0,>=3.2.2->wfdb==4.1.0) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from pandas<2.0.0,>=1.0.0->wfdb==4.1.0) (2021.1)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.8.1->wfdb==4.1.0) (4.0.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.8.1->wfdb==4.1.0) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.8.1->wfdb==4.1.0) (2022.12.7)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from requests<3.0.0,>=2.8.1->wfdb==4.1.0) (1.26.4)\n",
      "Requirement already satisfied: cffi>=1.0 in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from SoundFile<0.12.0,>=0.10.0->wfdb==4.1.0) (1.14.5)\n",
      "Requirement already satisfied: pycparser in /Users/petercharlton/anaconda3/lib/python3.8/site-packages (from cffi>=1.0->SoundFile<0.12.0,>=0.10.0->wfdb==4.1.0) (2.20)\n"
     ]
    }
   ],
   "source": [
    "!pip install wfdb==4.1.0\n",
    "import wfdb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e11ce5b6",
   "metadata": {
    "id": "e11ce5b6"
   },
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<p><b>Resource:</b> You can find out more about the WFDB package <a href=\"https://physionet.org/content/wfdb-python/4.1.0/\">here</a>.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d492e49f",
   "metadata": {
    "id": "d492e49f"
   },
   "source": [
    "Now that we have imported these packages (_i.e._ toolboxes) we have a set of tools (functions) ready to use."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d38297",
   "metadata": {
    "id": "e7d38297"
   },
   "source": [
    "### Specify the name of the Database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68491718",
   "metadata": {
    "id": "68491718"
   },
   "source": [
    "- Specify the name of the database on Physionet, which comes from the URL: https://physionet.org/content/pulse-transit-time-ppg/1.1.0/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "982b8154",
   "metadata": {
    "id": "982b8154"
   },
   "outputs": [],
   "source": [
    "database_name = 'pulse-transit-time-ppg/1.1.0'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49196a6",
   "metadata": {
    "id": "e49196a6"
   },
   "source": [
    "---\n",
    "## Identify the records in the database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b476f9b7",
   "metadata": {
    "id": "b476f9b7"
   },
   "source": [
    "### Get a list of records\n",
    "\n",
    "- Use the [`get_record_list`](https://wfdb.readthedocs.io/en/latest/io.html#wfdb.io.get_record_list) function from the WFDB toolbox to get a list of records in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d91aa6a7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d91aa6a7",
    "outputId": "db8e3169-76ac-4bdd-bbaa-91cf626c1a6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 'pulse-transit-time-ppg/1.1.0' database contains 66 records\n"
     ]
    }
   ],
   "source": [
    "record_list = wfdb.get_record_list(database_name)\n",
    "print(f\"The '{database_name}' database contains {len(record_list)} records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc82d67e",
   "metadata": {
    "id": "fc82d67e"
   },
   "source": [
    "### Look at the records"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29552f5a",
   "metadata": {
    "id": "29552f5a"
   },
   "source": [
    "- Display the first few records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb5745a7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bb5745a7",
    "outputId": "8fe32e59-c542-4a40-bd06-0c04fdcfbbfe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First records: \n",
      " - s1_walk\n",
      " - s1_run\n",
      " - s1_sit\n",
      " - s2_walk\n",
      " - s2_run\n",
      " - s2_sit\n",
      "\n",
      "Note the formatting of the record names:\n",
      " - subject (e.g. 's1')\n",
      " - activity (e.g. 'run')\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# format and print first six records\n",
    "no_records = 6\n",
    "first_records = [str(x) for x in record_list[0:no_records]]\n",
    "first_records = \"\\n - \".join(first_records)\n",
    "print(f\"First records: \\n - {first_records}\")\n",
    "\n",
    "print(\"\"\"\n",
    "Note the formatting of the record names:\n",
    " - subject (e.g. 's1')\n",
    " - activity (e.g. 'run')\n",
    " \"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b56c29d5",
   "metadata": {
    "id": "b56c29d5"
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<p><b>Q:</b> Can you print the names of the last five records? <br> <b>Hint:</b> in Python, the last five elements can be specified using '[-5:]'</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb21a93b",
   "metadata": {
    "id": "cb21a93b"
   },
   "source": [
    "---\n",
    "## Extract metadata for a record"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39dc9f3",
   "metadata": {
    "id": "c39dc9f3"
   },
   "source": [
    "Each record contains metadata stored in a header file, named \"`<record name>.hea`\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742071da",
   "metadata": {
    "id": "742071da"
   },
   "source": [
    "### Load the metadata for a record\n",
    "- Use the [`rdheader`](https://wfdb.readthedocs.io/en/latest/io.html#wfdb.io.rdheader) function from the WFDB toolbox to load metadata from the record header file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5a0afc5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c5a0afc5",
    "outputId": "13b3dfa2-d489-4a77-c07d-a5116d67b4ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done: metadata loaded for record 's2_walk' from the header file at:\n",
      "https://physionet.org/content/pulse-transit-time-ppg/1.1.0/s2_walk.hea\n"
     ]
    }
   ],
   "source": [
    "idx = 3\n",
    "record = record_list[idx]\n",
    "record_data = wfdb.rdheader(record, pn_dir=database_name)\n",
    "remote_url = \"https://physionet.org/content/\" + database_name + \"/\" + record + \".hea\"\n",
    "print(f\"Done: metadata loaded for record '{record}' from the header file at:\\n{remote_url}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a4d25d",
   "metadata": {
    "id": "f7a4d25d"
   },
   "source": [
    "---\n",
    "## Inspect details of physiological signals recorded in this record\n",
    "- Printing a few details of the signals from the extracted metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "58630149",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "58630149",
    "outputId": "e19d66b1-690c-4cc5-c754-c4b5d1b16d38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- Number of signals: 18\n",
      "- Duration: 8.2 minutes\n",
      "- Base sampling frequency: 500 Hz\n",
      "\n",
      "This record contains the following signals: ['ecg', 'pleth_1', 'pleth_2', 'pleth_3', 'pleth_4', 'pleth_5', 'pleth_6', 'lc_1', 'lc_2', 'temp_1', 'temp_2', 'temp_3', 'a_x', 'a_y', 'a_z', 'g_x', 'g_y', 'g_z']\n",
      "\n",
      "The signals are measured in units of: ['mV', 'NU', 'NU', 'NU', 'NU', 'NU', 'NU', 'NU', 'NU', 'C', 'C', 'C', 'g', 'g', 'g', 'deg/s', 'deg/s', 'deg/s']\n"
     ]
    }
   ],
   "source": [
    "print(f\"- Number of signals: {record_data.n_sig}\".format())\n",
    "print(f\"- Duration: {record_data.sig_len/(record_data.fs*60):.1f} minutes\") \n",
    "print(f\"- Base sampling frequency: {record_data.fs} Hz\")\n",
    "print(f\"\\nThis record contains the following signals: {record_data.sig_name}\")\n",
    "print(f\"\\nThe signals are measured in units of: {record_data.units}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f09b3f37",
   "metadata": {
    "id": "f09b3f37"
   },
   "source": [
    "See [here](https://physionet.org/content/pulse-transit-time-ppg/1.1.0/#description) for definitions of signal abbreviations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f921f27",
   "metadata": {
    "id": "9f921f27"
   },
   "source": [
    "### Find out how long each signal lasts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d217b764",
   "metadata": {
    "id": "d217b764"
   },
   "source": [
    "All signals in a segment are time-aligned, measured at the same sampling frequency, and last the same duration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c44f00a7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c44f00a7",
    "outputId": "1cfa789e-b66b-4c8e-805b-4197c663ba18"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The signals have a base sampling frequency of 500.0 Hz\n",
      "and they last for 8.2 minutes\n"
     ]
    }
   ],
   "source": [
    "print(f\"The signals have a base sampling frequency of {record_data.fs:.1f} Hz\")\n",
    "print(f\"and they last for {record_data.sig_len/(record_data.fs*60):.1f} minutes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a80895",
   "metadata": {
    "id": "d2a80895"
   },
   "source": [
    "## Identify records suitable for analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f2b5955",
   "metadata": {
    "id": "7f2b5955"
   },
   "source": [
    "### Specify requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f8611c",
   "metadata": {
    "id": "83f8611c"
   },
   "source": [
    "- Required signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d1505ab",
   "metadata": {
    "id": "3d1505ab"
   },
   "outputs": [],
   "source": [
    "required_sigs = ['ecg', 'pleth_1']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03920810",
   "metadata": {
    "id": "03920810"
   },
   "source": [
    "- Required duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "568a93c1",
   "metadata": {
    "id": "568a93c1"
   },
   "outputs": [],
   "source": [
    "# convert from minutes to seconds\n",
    "req_seg_duration = 1*60 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1036887",
   "metadata": {},
   "source": [
    "- Required activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14c307ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "req_activity = \"sit\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49187cd",
   "metadata": {
    "id": "d49187cd"
   },
   "source": [
    "### Find out how many records meet the requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "015b47d3",
   "metadata": {
    "id": "015b47d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Record: s1_walk   (not required activity)\n",
      "Record: s1_run   (not required activity)\n",
      "Record: s1_sit   (met requirements)\n",
      "Record: s2_walk   (not required activity)\n",
      "Record: s2_run   (not required activity)\n",
      "Record: s2_sit   (met requirements)\n",
      "Record: s3_sit   (met requirements)\n",
      "Record: s3_walk   (not required activity)\n",
      "Record: s3_run   (not required activity)\n",
      "Record: s4_sit   (met requirements)\n",
      "Record: s4_run   (not required activity)\n",
      "Record: s4_walk   (not required activity)\n",
      "Record: s5_walk   (not required activity)\n",
      "Record: s5_run   (not required activity)\n",
      "Record: s5_sit   (met requirements)\n",
      "Record: s6_run   (not required activity)\n",
      "Record: s6_sit   (met requirements)\n",
      "Record: s6_walk   (not required activity)\n",
      "Record: s7_walk   (not required activity)\n",
      "Record: s7_run   (not required activity)\n",
      "Record: s7_sit   (met requirements)\n",
      "Record: s8_sit   (met requirements)\n",
      "Record: s8_run   (not required activity)\n",
      "Record: s8_walk   (not required activity)\n",
      "Record: s9_walk   (not required activity)\n",
      "Record: s9_sit   (met requirements)\n",
      "Record: s9_run   (not required activity)\n",
      "Record: s10_run   (not required activity)\n",
      "Record: s10_walk   (not required activity)\n",
      "Record: s10_sit   (met requirements)\n",
      "Record: s11_sit   (met requirements)\n",
      "Record: s11_walk   (not required activity)\n",
      "Record: s11_run   (not required activity)\n",
      "Record: s12_walk   (not required activity)\n",
      "Record: s12_run   (not required activity)\n",
      "Record: s12_sit   (met requirements)\n",
      "Record: s13_walk   (not required activity)\n",
      "Record: s13_sit   (met requirements)\n",
      "Record: s13_run   (not required activity)\n",
      "Record: s14_run   (not required activity)\n",
      "Record: s14_walk   (not required activity)\n",
      "Record: s14_sit   (met requirements)\n",
      "Record: s15_walk   (not required activity)\n",
      "Record: s15_sit   (met requirements)\n",
      "Record: s15_run   (not required activity)\n",
      "Record: s16_sit   (met requirements)\n",
      "Record: s16_run   (not required activity)\n",
      "Record: s16_walk   (not required activity)\n",
      "Record: s17_run   (not required activity)\n",
      "Record: s17_walk   (not required activity)\n",
      "Record: s17_sit   (met requirements)\n",
      "Record: s18_run   (not required activity)\n",
      "Record: s18_walk   (not required activity)\n",
      "Record: s18_sit   (met requirements)\n",
      "Record: s19_walk   (not required activity)\n",
      "Record: s19_sit   (met requirements)\n",
      "Record: s19_run   (not required activity)\n",
      "Record: s20_run   (not required activity)\n",
      "Record: s20_walk   (not required activity)\n",
      "Record: s20_sit   (met requirements)\n",
      "Record: s21_walk   (not required activity)\n",
      "Record: s21_sit   (met requirements)\n",
      "Record: s21_run   (not required activity)\n",
      "Record: s22_walk   (not required activity)\n",
      "Record: s22_run   (not required activity)\n",
      "Record: s22_sit   (met requirements)\n",
      "A total of 22 out of 66 records met the requirements.\n"
     ]
    }
   ],
   "source": [
    "matching_recs = {'dir':[], 'name':[], 'length':[], 'start_sbp':[], 'end_sbp':[], 'delta_sbp':[]}\n",
    "\n",
    "for record in record_list:\n",
    "    print('Record: {}'.format(record), end=\"\", flush=True)\n",
    "    \n",
    "    # check whether this record corresponds to a suitable activity\n",
    "    if not req_activity in record:\n",
    "        print('   (not required activity)')\n",
    "        continue\n",
    "\n",
    "    record_data = wfdb.rdheader(record,\n",
    "                                pn_dir=database_name,\n",
    "                                rd_segments=True)\n",
    "\n",
    "    # Check whether the required signals are present in the record\n",
    "    sigs_present = record_data.sig_name\n",
    "    if not all(x in sigs_present for x in required_sigs):\n",
    "        print('   (missing signals)')\n",
    "        continue\n",
    "\n",
    "    seg_length = record_data.sig_len/(record_data.fs)\n",
    "\n",
    "    if seg_length < req_seg_duration:\n",
    "        print(f' (too short at {seg_length/60:.1f} mins)')\n",
    "        continue\n",
    "    \n",
    "    # This record does meet the requirements, so extract information and data from it\n",
    "    \n",
    "    # Information\n",
    "    matching_recs['dir'].append(database_name)\n",
    "    matching_recs['name'].append(record_data.record_name)\n",
    "    matching_recs['length'].append(seg_length)\n",
    "    \n",
    "    # Blood pressure measurements\n",
    "    str_to_find = '<bp_sys_start>: '\n",
    "    curr_len = len(str_to_find)\n",
    "    start_el = record_data.comments[0].index(str_to_find) + curr_len\n",
    "    str_to_find = '<bp_sys_end>'\n",
    "    len(str_to_find)\n",
    "    end_el = record_data.comments[0].index(str_to_find) - 1\n",
    "    matching_recs['start_sbp'].append(int(record_data.comments[0][start_el:end_el]))\n",
    "    str_to_find = '<bp_sys_end>: '\n",
    "    curr_len = len(str_to_find)\n",
    "    start_el = record_data.comments[0].index(str_to_find) + curr_len\n",
    "    str_to_find = '<bp_dia_start>'\n",
    "    len(str_to_find)\n",
    "    end_el = record_data.comments[0].index(str_to_find) - 1\n",
    "    matching_recs['end_sbp'].append(int(record_data.comments[0][start_el:end_el]))\n",
    "    matching_recs['delta_sbp'].append(matching_recs['end_sbp'][-1]-matching_recs['start_sbp'][-1])\n",
    "\n",
    "    print('   (met requirements)')\n",
    "\n",
    "print(f\"A total of {len(matching_recs['dir'])} out of {len(record_list)} records met the requirements.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "719f20f8",
   "metadata": {
    "id": "719f20f8"
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<p><b>Question:</b> Is this enough data for a study? Consider different types of studies, e.g. assessing the performance of a previously proposed algorithm to estimate BP from the PPG signal, vs. developing a deep learning approach to estimate BP from the PPG.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24327602",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5b06e8",
   "metadata": {},
   "source": [
    "- Import the _matplotlib_ package for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86de4191",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d67179ce",
   "metadata": {},
   "source": [
    "### Blood pressure measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08196d60",
   "metadata": {},
   "source": [
    "- Display blood pressure measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e17ab6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp = np.concatenate((matching_recs['start_sbp'], matching_recs['end_sbp']))\n",
    "plt.hist(bp)\n",
    "plt.xlabel('Systolic blood pressure (mmHg)')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1644087e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp = matching_recs['delta_sbp']\n",
    "plt.hist(bp)\n",
    "plt.xlabel('$\\Delta$ Systolic blood pressure (mmHg)')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de248318",
   "metadata": {},
   "source": [
    "- Display changes in blood pressure measurements between start and end of activity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7fe94e3",
   "metadata": {},
   "source": [
    "### Inspect signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6fccda20",
   "metadata": {
    "id": "6fccda20"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60 seconds of data extracted from segment: s1_sit\n"
     ]
    }
   ],
   "source": [
    "start_seconds = 0\n",
    "n_seconds_to_load = 60\n",
    "rec_no = 0\n",
    "record_name = matching_recs['name'][0]\n",
    "record_dir = matching_recs['dir'][0]\n",
    "record_data = wfdb.rdheader(record_name,\n",
    "                            pn_dir=record_dir,\n",
    "                            rd_segments=True)\n",
    "fs = record_data.fs\n",
    "sampfrom = fs * start_seconds\n",
    "sampto = fs * (start_seconds + n_seconds_to_load)\n",
    "\n",
    "segment_data = wfdb.rdrecord(record_name=record_name,\n",
    "                             channel_names = ['ecg','pleth_2'],\n",
    "                             sampfrom=sampfrom,\n",
    "                             sampto=sampto,\n",
    "                             pn_dir=record_dir)\n",
    "\n",
    "print(f\"{n_seconds_to_load} seconds of data extracted from segment: {record_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8ff4b753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig_no = segment_data.sig_name.index('pleth_2')\n",
    "ppg = -1*segment_data.p_signal[:,sig_no]\n",
    "fs = segment_data.fs\n",
    "t = np.arange(0, (len(ppg) / fs), 1.0 / fs)\n",
    "\n",
    "sig_no = segment_data.sig_name.index('ecg')\n",
    "ecg = segment_data.p_signal[:,sig_no]\n",
    "\n",
    "fig, axs = plt.subplots(2, 1)\n",
    "axs[0].plot(t, ppg)\n",
    "axs[0].set_xlim(0, n_seconds_to_load)\n",
    "axs[0].set_xlabel('Time (s)')\n",
    "axs[0].set_ylabel('PPG')\n",
    "axs[0].grid(True)\n",
    "\n",
    "axs[1].plot(t, ecg)\n",
    "axs[1].set_xlim(0, n_seconds_to_load)\n",
    "axs[1].set_xlabel('Time (s)')\n",
    "axs[1].set_ylabel('ECG')\n",
    "axs[1].grid(True)\n",
    "\n",
    "fig, axs = plt.subplots(2, 1)\n",
    "axs[0].plot(t, ppg)\n",
    "axs[0].set_xlim(0, 3)\n",
    "axs[0].set_xlabel('Time (s)')\n",
    "axs[0].set_ylabel('PPG')\n",
    "axs[0].grid(True)\n",
    "\n",
    "axs[1].plot(t, ecg)\n",
    "axs[1].set_xlim(0, 3)\n",
    "axs[1].set_xlabel('Time (s)')\n",
    "axs[1].set_ylabel('ECG')\n",
    "axs[1].grid(True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40723e1a",
   "metadata": {},
   "source": [
    "### Plot frequency spectra of signals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ea920f",
   "metadata": {},
   "source": [
    "Now we're going to plot the frequency spectrum of the PPG signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "fc575ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef4bfb51",
   "metadata": {},
   "source": [
    "- First, let's filter the signal to eliminate very low frequencies which would otherwise dominate the power spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "9cdcf9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7faa795c9070>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify cutoffs in Hertz\n",
    "hpf_cutoff = 0.5 \n",
    "\n",
    "# create high-pass filter\n",
    "sos_ppg = signal.butter(4,\n",
    "                    hpf_cutoff,\n",
    "                    btype = 'highpass',\n",
    "                    analog = False,\n",
    "                    output = 'sos',\n",
    "                    fs = segment_data.fs)\n",
    "\n",
    "#  high-pass filter\n",
    "ppg_filt = signal.sosfiltfilt(sos_ppg, ppg)\n",
    "\n",
    "# plot results\n",
    "fig, axs = plt.subplots(2, 1)\n",
    "axs[0].plot(t, 400+signal.detrend(ppg), color='blue', label='raw')\n",
    "axs[0].plot(t, ppg_filt, color='red', label='filterd')\n",
    "axs[0].set_xlim(0, 20)\n",
    "axs[0].legend()\n",
    "\n",
    "axs[1].plot(t, signal.detrend(ppg), color='blue', label='raw')\n",
    "axs[1].plot(t, ppg_filt, color='red', label='filterd')\n",
    "axs[1].set_xlim(10, 12)\n",
    "axs[1].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e70585",
   "metadata": {},
   "source": [
    "- Let's plot the frequency spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1f5d4819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pxx, freqs = plt.psd(ppg_filt, NFFT=1024, Fs=fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a585082c",
   "metadata": {},
   "source": [
    "- Now let's focus on the lower, physiological frequencies, by downsampling the signal to a lower sampling frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "817fee25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QrOCw0B1SuiXieej1ghE494XADcBl7rDVjSJyNfBdEdkqIpuBKzk5nDZpTrQ66zXHqjlMd1dkqzrRv1P6RGsXXT3BqDd4cGoOQNThrIcbB645gDMBD6BxgOBwuLF/Z3ZRhlDT0MGzu48POOtZVdl9tIW5pSeDgzMBUNgfZ8K/B149zDce2s6j247E9X5jzNgRq89hUSJPrKrPEX0t6n8k8rzDcXIVtoFrDqHZy0ei3OAPhpb4LIp+fGhC29EoE9qiTYCLlOsGh+aBag6NvbO5AhRleHimuokbfvUKt71nGVefUdHvuKPNnTS0d/ea9JfiEaYVZHKovp3/+scOgqrc8sbTop4X4C8bqgF49WAjb1o6ZcD3GWPGnlgpuw+MZkHGstCwz2mFA9ccQmm8G9r6D0mNXP85mlCz0rEoE9oON/kpiugz6MuXloIvLfpKdM3+blo6A/0Cizfio7bWNEUNDqF5HX1nhE8rzORgfTsbDzaS5U3lljf2OxRwrvnl/fUAbDjYEP1NxpgxK1ZW1hYRaR7oMZqFTLbQzX1K/sA1h7yMNESgPkrHcKjmMFDNoygrnVSPRG1Wqm3qPwGur/yM6CvR1bpNUpV9mqROL3KiQ35mWjgI9LXd3b6wz4zw6YWZ7D7ayuEmf8xZ3f/YUgvAm5ZWsv1ws/U7GDPOxEqfkaOqucCPgC8DU4CpwJeAb49K6caI6oYOirO9ZKQPPL4/xSPkZ6RFrTkcrG+nPNc34Ld/j0cozfFypKl/s9KBuvaYHeEQWmyof3AId2b3CS5LS1PZ/Z03cOmC0nAQ6Gt7bTNTCzLI7ZNIcEZRZnj9iWZ/YMCb/rO7TzC/LJtrl1QSCCpbaiz1hjHjSTwzpF+vqrepaouqNqvqzxkjw0tHS1VdW8wmpZCCrHTqozTvHKxvH7BJKaQ019evWam1M0BVXVvUZH+RcjPSonZI7z3WCsDMoqx++9JSPCyqyOFoc2d4hbtIO2qbo553Wp/riDbCyt/dwytV9aycV8JSdwb42qr6mNdgjBlb4gkOPSLyHhFJERGPiLwHmFRtBK8dbWV+af+Ee30VZqYP2OfQ96baV1mut18zza4jzahGz+cUKT8jLWqH9I7aFkpzvBRlR5+WclpFnvu+3rUHf3cPVSeiB6UZfTrVo/WTrK2qpysQ5KJ5xRRne1lUkcuaXcdjXoMxZmyJJzj8C/AO4Kj7eLu7bVI40ep8s55fPnhwKMhK7/ct3N/dw5Fm/6A1h7JcX7/RStsPu8n+KgcJDplpUfscdh5pjpoLKiSUYbZvcKhp7CCoztyNvkJNXMXZTgd8tJThz+05QXqKh3NnOQsGXrawhPUHGuJK9meMGRsGDQ6qWqWqb1LVYlUtUdU3q2rVKJQt6bZUN7HVbSufX5Y9yLvdmkOfZqWaxg5UYXpR7GapslwfTR3dvdrwt9c2k5+ZNuAw1pC8jDQaO3qft7snyO6jrSyKEdSKsr2U5nj79TuEZmWXRzlvljeVZdPzedvyaQBRJ/1tOtTIospcMt3FjC5bWEpPUHl2t9UejBkvYo1W+mqstaJF5DIRuSYxxUq+Yy1+3vSz5/jyX7YAsKAsvppDQ1vv5UL3HXcmjM2I0u4fKbTsZ+Q38e21LSwqz8WZTD6w/Mx0/N3BXoFl/4k2unqC/UYb9bWoIpcdbsbZkFBqjcq86AHt/o9fyBdfv4AUj/RrVlJVth1uZnFEbWfptALyM9N4YvvJmdWDLTnaGeiJe80JY8zIi1Vz2AI8JCJPisj3ReSLIvI1Efm9O0v6WtwsqhPR1pomgupMasvLSKMkZ/B0UoVZaXT1BGnrOnmT3nXE+VY+f5DgEprrcNS92XYFguysbR60SQmiT4QLNRUNtqznoopc9hxroStw8mYdq+YQ4vEIxdnp/ZqVDtV30OIPsHhKXnhbikd4w+JyHtt+lPauAP/cUsvcW/7J/iizycEZvnvhd5/ivb96OWpnuTEm8WINZX1AVS8EPgZsA1Jwciz9H3COqn5WVSdsO0GovR+cWsNg394BCjL7T4TbeaSFaYUZZHtjp8cOBwe3U3rr4SY6A0FWzCgY9LzRUmg8u/sE3lRPr9xI0SyqyKG7R9njjmyC/sn6BlKa4+vXrLT1sNMMd3qfoPbmpVNo7+rh8e1H+cKfNgEnA2ekYFD53H2baOvsYW1VA7f8dUvMMhhjEiP2HQtQ1d3A7lEoy5iy7XAzM4oyyc9M56J5xYMfwMlZ0vVtXeHRSbuOtLCgbPBv/+W5vVNovOLOLj571oAte2GhBYQON3YwvyyHvcdbuX9DNe+/YCbpqbG7lUIjoXZE1FJqmzoG7ecApynscFPvZqVth5tI8Ui/mtLZMwuZkp/BrY/sCtes6tv6d1A/vKWWF/fV8d3rz+C5PSdsfoQxSRL3SnCTjdNunscDn7iQT18+b/ADcPocgPBch85AD/tOtLGgfPDO7NyMVLypHo65NYdX9tczpySr18pxA1kyNY8Uj4QDyv8+tQdfWgqfuHTuoMfOKs4iPdXTa8SSk0F28Hkdpbk+qhvae9WUttY0M680u1+tw+MRvvrGRXhTPeFEg33nSPQElZ88uZt5pdm8Y8U0ZhZlUd3QETWVuTEmsSw4RNHs7+ZgfXtc7f2RCvs0K+091kZPUFkwSLs/OKm0y3J9HGn20xNU1lbVc86sorjOm+VNZcnUPF7aV0eLv5t/bKnl+mVT4gosqSkeFpbnsC2iGc3J5Dp4zeG6MyvpDAR5x+0v0tbprBq3/0Qb8wboX3nDGRU89YVVvPxvV1CYld6vM/vZ3cfZc6yVT18+D49HmF6USU9Q41p7whgzsgYNDrFGLE1Uu444o3cGm3zWV0FEsxLArqOhTuHBRzrByYlwO2qbafEHwvME4nH+7CI2Vzdx/4YaOgNB3nLW1LiPPWNKntMBH1TaOgM0+wOD5nMCOH9OEbe/dzm7j7Xyt4014Rt5rNTmISXZ3n41h1AOq9B1h2Z2V9W1x30txpiREU/N4WUR+ZOIXC3x9MpOAKFO4b4J6waT60slLUWoc4PDy/vqyfGmMrs49jDWkNJcH8eaO3l29wkALpgbX80B4LzZRQSCyg8f28WMokyWuWkr4nHm1HxaOgPsr2uj1u1DGGgYa1+rFpRwWkUuv3/xAMda/ASCGk5fHktprrdfZ3bo5xYKsqHZ2AfjXD/CGDNy4gkO83HWar4B2CMi/yki8xNbrORqCN+k0gZ5Z28iQmmOj6PNflSVNbuOs3J+Makp8bXeVeT6qGns4JFtR1hQlhNe5yEeZ88s5IwpeRRne/nUZfPiGl0VcsZUZ9jpluomtrmjjQZL9xEiItxw/gx2Hmnh4U1OJtbh1hzq27rcAOv8vEpzvPjSPHHXHFQVVSXQE+REqy1lasypiGe0kgKPA4+LyKU4Q1k/LiKbgC+r6osJLuOoC3+DdfsQhqI018ux5k52HmnhSLOfVfNL4z72LcumcOdz+9l0qJEPXTRrSOfNSE/hoU9dNNTiArgdyB42VTey73gblXk+lk7Lj/v4Nywu5yv3bwkv7hNXcMh1goOqhgNZXVtXrzxQIsKMwiwOxBkc3nn7S5wxNY/UFOGelw+y9qtX4E2NPRzXGBNdPH0ORSJys4isA74AfAooBj4P/CHB5UuKhj7fYIeiPNepOazedQyASxaUxH3s6ZV5vHGJs/BOvMNnR0JqiofTK/N4cscxnt19nLcun0qKJ/6aR35mOjOKMtnp9tXEWvcipDTHR1dPsFe+pfrWrvBw4JAZRZlU9WlW+tBda/nhY7t6bQv0BFl/sIHfvVjF/714gGZ/gNeOtGKMGZ547n4vArnAm1X1jap6v6oGVHUd8IvEFi85+n6DHYrQiKONBxuZXZIVntwWr1uuXsRHL57NhXNGLzgAvPuc6eGZ0W9dFn9ndsiSqfmAs3BRrHUvQkIzzq/+8bP8/iVn0cH6tv7BYfGUPPYebw13Vu+obebJncd4eV/vFOC1Tc4or+4eDc+jCM2R2HOslfbu2Kk49hxr5Y9rD0VdUc+YySie4PBVVf0PVa0ObRCRtwOo6vcSVrIkinaTildprpcWf4Bth5uZO8js5Ggq8zP4ytWLBp28NtLetnwqL/3b5Tz8qZXMjLMDPdISN11GPE1KcDKX1OEmP4+7OZfq2roo6vNzf8eKaXhE+D83gNy39hAAtc29h7eGgsey6fm8aWkleRlpbKlpQlV5689f4M+vDXzTf2BjDa//0TN88S+bWfm91VQNkNbDmMkknjvQl6Ns+8pIF2QsqW/rGlZ/A5yc6VzT2MGc0qEHh2QqzEof8tyOkCVup/aUOINDZK6q3UdbCAaVhvb+Qbk8z8dVp5dz79pDPLrtCPe7/RpHmzp7JeY71OAEhx+/6yx+/K6zWDwll601TRxt7qSpo5sd9QMvQfLI1iOU5Xj53Y3n0NIZCAcrYyazWFlZ3yAiPwWmiMhPIh53AYFRK2ES1Ef5BhuvyGakeIewTgSnT3FmaU8vjO+apxdmcs2SCq4+o5zaJj81jR30BDVqje2Tl80lxSN89Pfr8aWl8N7zptPVE+yVlO9gfTupHglP3ls8JY+dR5rZ6eZvqm1T6gYYwXSspZOZxVlcPL+EmUWZvDLEVesO1bdbBlkz4cSqORwG1gF+YH3E40Hg9YkvWnKout9gs4cbHE5+Ix5vNYdTke1N5Q8fPpePrIxvlFVaiof//Zdl4cl6odQfRVF+7osqcnnk5pV8+rK5/O0TF3LRXKeT/1BDBwfdkUwH6zuozM8IDxteMiWf7h7ln1uOhD9n3YGGqGU51uIPN3OdM6uQtVX1BIPx3ex3HWnh4u+v5p9bjwz+ZmPGkVhZWTep6m+BOar624jH/aoa/a9sAmjpDNDdo+FUGEMVWXOYUzx5ggPAubOLhtyRP88NoC/tqwOgMCv68aW5Pj535QIq8zPCtYPv/XMnr/ufp2nxd3OozzrdK2Y62Wwf3nwYb6qHVA88uOkwrx3tvXaFqnK8pTPczHXOrCIa27vZfSy+kU6PbTuCKqxxR6cZM1HEalb6o/v0VRHZHPHYIiKbR6l8o66+1WmqGG6HdLY3lcz0FIqz08nLHNokusloWmEm3lQPL4dqDnH83EPrTLy4r47OQJCaxg53ne6T/R1luT5mFGXS1tXDrOIsFhR4+PvmWt78s+dp9p8cPtvSGcDfHQxPOAyl7lgbZ9PSkzudoPDC3rq43m/MeBFrEtzN7r8TdrW3aEIZVYcbHEIJ9EqGORR2sknxCHNKssNLlcbzcy/O9pLiEXrcpp/dR1upa+tiakHv+RVnzyzkQF07s4qzuK68By1byMfv3sADr9Zww/kzgZOZYUM1h6kFGaSnejhY3+4OjQ0OuK7FidZONlU3Upnno7ohFKDim1luzFgXq1mp1n16AjikqgcAL3AmTn/EhHSqNQeAL121gM9cEV+abwPXL5sSfh7Pzz3FI5RFjHZ6Ya+Ti2pWnwEA58wsDG/PSHVWozu9Mpe7Xz5Id0+QN/3see58dh9wcmitiNOpXdvk56t/28obf/IsHV09+Lt7+Ox9G3s1Sz27+ziq8IXXL+hVjqG4f0M11/z0Wd51x4tx93MYMxriGcr6DOATkSnAk8AHgbsSWahkCo2AOZXgcNXiCi6YO7qT2MazD6+czRevWsDlC0sHXX0upCwipXgoUeHskt7B4fw5RaR6JLxkqYjwL+dOZ+eRFn6xZi+bDjXyl/U1QO+hteW5PmobO9ha08Te42388LFdvLivjr++WsPfXq0Jv++V/fXk+lJ509IplOZ4eWb30IPDbWv28tqRVl7aV89rx1oGP8BEtXrXMf647lCyizGhxBMcRFXbgeuBn6rqW4DTElus5Glo750Z1IyOj6+ay68+cHbc76/MzyA/M40p+RlUN3QgcjLFd8i0wkye+9JlvGFxeXjbdWdWkpmewo+edBY37HIXEopMcliZn0Ftk5+D9e2kp3j49fP7ududhLe5+uTKdC/vr+fsmYWkeIRVC0p45rXjQ1qY6ERrJ3uOtfL2Fc6IrZes32LYblu9h5+t3pPsYkwocQUHETkfeA/wd3fboAn7xqsWfwCPQFYcKSBM8nz+dfP55ftWhGdkT8nPiFrrKM/z9cpQm+NL47ozK+kJKunusNf0FA+5Gam9jjnc1EFTRzc3nD8DEeGJHU7H86bqRoJBZ4TTvuNtnON2YF+2sJQWf4D1AwyXjWad2+l9/bKpTC3I4KV9Q5tfYRyqys7aFlr9E3r61aiLJzjcjDMj+q+quk1EZgOrE1us5Gnxd5PtTR1Symsz+maXZDvrUrvBYfYQUpW897wZpHiEmy6eDThNSpG/78o8H6E5befMKuTqM5xkiKdV5NLiD1BV1xYezRQKDhfNKyEtRVi9M/4hrS/vr8eX5uGMKXmcN7uIl/fXWb/DMFQ3dNDSGaDFgsOIGjQ4qOozqnpdKI+Squ5T1U8nvmjJ0dIZIMdnQ1DHi9DCQkOZjb54Sh4bvvo6PnnZXFI90qu/AaA8YqGj6YWZfHzVHBZV5PLFq5yO5z+uq+bWR3aSl5EW7s/I9qZyzqxCnhpKcNhXz7LpBaSnejh3ViEN7d3sPW6ZZIcqtP55V0+QbguuIyaelN3zReQOEXlMRJ4KPRJZKBHxicgrIrJJRLaJyDcTeb5ILf4AOb4J22o24YRW65tTMrRUJXmZafjSUjh3diEL+qx5Hbl+9vTCTBZV5PLPm1eycl4J2d5UfvH0Xpr9AX71/hW90rpfuqCU3cdOZpAN+cQfNvDVv20BnCaQPcdaOFTfzvbaZlbOc2Z7L3TXGd973JL+DdWO2pMd+REZ4M0piucu+Cec1Nx3AgNnLxtZncBlqtoqImnAcyLyT1V9KdEnbrXgMK6EOqEXlA8vYeBvPnAOfZeuCAWH4mwvWd6T/xdSPMJvbzybutYuVsws7Dei7bKFpXz77zt4aucx3n/BTAB6gsoT24/SGQhy7qwiCrPSec+dL4eXQL3GXb9jZrHzev8gGWGf3X2cHF/akBZjmuhCNQeAjoDVHEZKPHfBgKr+POElieCuPheqX6e5j1H5rbd0dg9peU6TXOfNLuT+j1/AWcO8WUZLjV6YlU56qofphf0zzC6fUTjgZ80uyWZmUWav4HCgro3OQJBUj/A/j7/GB90V/g7UtbNsen540lyOL42SHC/7TwzcrKSqfPa+jcwsyuLP/++CoVzmhPbasRa8qR46A0ELDiMonuDwkIh8HPgrzjd6AFQ1oUMrRCQFJ9HfXOBnqvpyn/03ATcBlJWVsWbNmmGfq7W1NXz88YZ2soNtp/R5Y13k9U4UT++LvX+o11yZCQUM/ed0Wm43j+w+zj0PP0VFtod1R5xO0oUFHrbXtfHCJmcFuxSBM7Lbe31+YWo3G/ceZs2a6COealqCnGjtorm9iyefWj3oan3RrllVWXukhzNLU/CmTIxBF8ca2yjyCYdbob6lY8L93x5Mov6e4wkO73f//deIbQrMHvHSRJ5AtQdYKiL5wF9FZLGqbo3YfwdwB8CKFSt01apVwz7XmjVrCB0fePZx5kwvZ9WqM4Zf+DEu8noni6Fe80PndZOW4ol7Ul7I4hWdPH3ratY05HL7NSvY9MRuRF7jbRcs4BsPbedoTxZTCzr5+6dWkpvRe1TcI3WbeWLH0QHLedfz+4HtdAUhfdpiqk60cdXiin4d6iHRrvmV/fXc9uiLfPLSuXzh8gVDuraxSFXpeOyfrJhSzOFdx9E0n/3fHiHxjFaaFeWR0MDQ5/yNwBrgqtE4X0tngGzrc5j0cnxpQw4M4PRTfOySOTy67Sj3vHKQ1462ML0wM9wnsrm6iSn5GeRlpvUbLj2rOIsTrV291tWO9OK+uvD8m0/d8yr//sA2Vt76VL9Ms7G86E60u+uFqgmxJGprZ4CeoIbnu/itWWnExDNaKVNEvioid7iv54lIQpPxiUiJW2NARDKAK4CdiTwnQGegh65AkFwbympOwcdWzeGS+SV89W9beea14ywoy2G62wEdCOqAq+WFckNFW6bU393Di3vruPqMCnJ9qTS2d/PGMyroCSp/Xl/d7/0DeXl/HSU5Xlo7A+GlV8ezUCCdku/8fNttqsOIiWcS3G+ALiDUA1YNfDthJXJUAKvd1OBrgcdV9eEEnzM8wzLbazUHM3xpKR5ue88yLl1QQktngEUVuZTn+sIzsqfmDxIc6k4Gh73HW7n1kZ38+MndNPsDXL9sKmdOy8cj8KWrFnLR3GL+vrk2rpXougJBNhxs4JolFSybnp+w5VBb/N18/o+bONrsT8jnRwoHBzfgWof0yInnLjhHVd8pIu8GUNUOSfD0YVXdDJyVyHNEE5phaUNZzanK8qbyy/et4Pk9dZw5zVlCdWpBBvtOtA1YcwiNXAqtbrf/RBvvvP0lTrjLm162sNRJJpgiXLOkgulFmVyzpJLP/2kTGw81ctb0gl6fV9XUQ6AnGF4db1N1I/5uZ0htUVY6P3jsNXcVvJEdnffc7hP8ZUM188uy+eglc0b0s/tq7nD+Zouz0vGmerBJ0iMnnppDl9u0owAiMoeIUUsTSYvVHMwIEhEumlccnnEfaloKNYH05UtLoSzXy0F3Et2vnttHW2eAn777LK46vZx/v8bJd3n2zELeefZ0AK5YVAb0X2zoYF0733zRz30RmUpvW72HbG8q588u4rKFznFrdh3vV47n95zg75trae0c3p1246FG4GS23EQK1RxyM9LI8aXSbjWHERNPcPg68AgwTUTuxknb/cWElipJWjqd/2iWPsMkQmgZ04FqDgDTCjLDwaG6oYPZJVlce2Ylv7hheb/1KsCZ6V2Qmcbhxo5e2zccbECBte4Ke6t3HmP1ruPcfPk88jLTWFSRQ0Wej6cjgoOq8t+Pv8Z77nyZT/xhA++8/cVwc5WqctPv1nH703sHvc5X3eDwSlU9/u7EzpttdoNDXkYa2d5U65AeQfGMVnocJ133B4B7gBWquiaxxUoOa1YyibRiZiGVeT4q8wduxplemEl1g3Ojr230U5E3cCAJqczPoKZPcNhU3QicvFH/eX01pTne8OQ8EWHZ9AK21JxMQX7v2kP85MndvH35VL7yhoVsO9zMhoPOnIvN1U08tv0o3390V8zRUYGeIFuqm5hTkkVXIMgr+xObaTZUc8jLTCPHl2Yd0iMoZnAQkVQRuRa4kZNrODQmulDJ0mrBwSTQdWdW8sJXLsebOvAQ2WmFmRxu6qArEORwU0fMQBIyJT8jXHPwd/dQdaItvO7Egbp2TrR28tK+Oi6aW9xrRviiihwO1rfT4u+muqGdrz+4jZXzivnuW5fw3vNmkJmewp/WOSOh/rjuEL40Dzm+VD59z6sDdja/drSVju4ePrxyNh5hSCnMh6OpoxuPQHZ6qtUcRtiAwUFEKoFtwOeBSmAKzkS4be6+CafFXXje+hxMskwrzEQVXjvaQos/EH/NoaEDVeVnq/ew6gdr2FLdxIxc58/7j+sOUdfWxXlzinodt6jCmXvx2tEWXtlfT1cgyFffeBopHiHLm8obFlfw8OZa2rsCPLjpMFcvruDH7zqLg/XtvP0XL9LR1b/JKNTfcP7sIoqzvdQ2dfR7z0hq9neTm5GGxyNOn0O3BYeREqvm8J/Az1V1lap+VlU/o6qXAD8D/mt0ije6Qh1wNgnOJEuoX+JltzkmnprD1IIM2rp6aO4IhBP3dfUEuXRaKqke4VfP7gecG3akhW5w2F7bwo7aZtJTPb2y2y6bkU9rZ4CNhxpp8QdYOb+Yi+eXcOf7VnCwvp1fPefkLHntaAtt7t/OxkMNFGSmMaMok4o8H0eaEzt2pamjOzwvKduXSoc1K42YWHfB81T1A303qupPRGRX4oqUPC3+AOmpnpjVfmMSKRwc9jmjj+KtOQDUNDrNUeDUCpaWdJNbPoOfPLmbqQUZ4aGy4ePyfOT6UtlR28yBujYWlueEh73CyaVTt9U4WU/L3NcXzC3mytPKuG3NXrbXNvOPLUc4d1Yhf/jIeWw81MiZ0/IREcpyfVTVteHv7qGjqychS+82dXSTl+EEh1xfGh0BRVV5cNNhVi0oDe8zQxer5hCrPtgeY9+41dIZINdqDSaJSnO8eFM94ZpD5NoSA5kSERyOt3aycl4x/7x5Jfk+D5973XzuuGE5/3V9/1xhIsKiilx21Dazo7aFRX3Snpe6OZtCndaluSdzOH3t2tNYNr2A1TuPs3JeMS/vr+fWR3ey+1hrOJ14RZ6P2iY/33p4O2d/5wn+9U+b+qUkDwaVZn/vdCFf/dsWvvHgtkGvG3oHh2yvU3N4bs8Jbr53I38Zwsxx01+sO2GeiFwfZbsAw0ueP8a1+gO98vcbM9o8HmHVghIe3XYUEWc968GEag6HGzs43tLJrKLeQ16vPL18wGPPnlnIz9bsQdXpoI4UCgZbD4eCw8myTC3I5P8+fG54qOun7nmV293UuKHgUJbno8Uf4Pk9J8jPTOfBTYf528Ya/vHplcxzF1j678df4/9ePsBLX7kcX1oK9W1d3PvKIcrzfHzjutMHvfamjm4q3drV6ZW5KPC5P24CYF+M9OdmcLFqDk8D10Z5XAM8k/iijT5/dw8Zw0i2ZsxIetvyaQCUZHt7rTQ3kOJsZ/2JGjc4FA+QpTWaj6ycTUGm09xzWmVen8/1IuLM1PaleciJ8sVJRBAR/v2a08h0kwJG1hzAGTH11uVTeOyzF9Pdo+GJdw1tXfz6+f00tnez1a2d/H3zYQJBpaaxg5f21XHZD9fw1M7eaT72HW/lh4/torqhneaOALluzeGqxeXMzfdwvMXp5xhs4SQT24Bfk1X1g6NZkLHAHwjiteBgkmzVghKKstLDNYLBiDipObYdbqIzEKQkO/7gkJeZxteuOY3vP7qL0yp7NwikpXgozEynrq2L0hxfvyyykcpynW/666rqyXeDTVlETWNBWQ4zirKYWZTJ2qp6PnLxbH7zQhXt7ointVUNrJhZyF9frUEEVOGXz+xj3/E2PvTbddz1wXO4ZH4JDW1dfOA3azlY384vnt5Ld4+Sm5Ea/jnccFo69x/y4Uv1UHWinSe2H6W9u4frznQGWPYElX+7fwuXLyqNWaMy8c2QnjT83T14o6wMZsxoSkvx8KN3LeVLVy2M+5j5pTmsq3LmFAy0vsNA3nzWFJ7/8mVRh3CHPqs0js98x4pp3Pq2M8OvIzvTF5Q7zUgrZhay7kADPUHlT+sOcemCEuaUZLHWnU298VAjq+Y762o/7Wa0nVGYyX/+fQc9QeXWR3dxpMnPL967jIvd9beLIjq6Z+Sm8MAnLuTCucXUNHbwtQe28oU/bgrnq3pwUw33rTvEF/60iWMtiU8MOJ7ZnTBCZ3fPsHL4GzPSVs4r4fw+8xJimV+eQ6c7UmmowSGW0GdF1gLiVe4e4xGYU5INwNkzC6hv6+JP6w5R2+TnzWdN4eyZhayrqmfXkRaCCm9c4nzLDwSVZTPy+dfXL2TX0Rbu31DN49uPcOXpZVy1uII737+Cuz98Lu85d0a/c890U40cbvLT1RPke4/spLsnyI+e2M2s4iz8gSBv/fkL3PnsIEsITmIWHCL4u4P4rOZgxqEFZSc7k0cyOISGsw7nMzPSU8jLSGNmcVb4S9eKmc4a3N99ZCfeVA+XLypjxcxCmv0BHth4GIBl0/MpczvDz5iSz9VnlLOwPIfv/GMHJ1q7uHxRKeA0I104tzjqIJLZEXmorjytjH9urWXt/noO1LXz2dfN56fvPovCzHS+/fcd4VqF6S3WDOnrYz1Gs5CjxR+wmoMZnxaUZ4efD6XPYTChEUuRw1iHYmF5DmfPKAy/nl2cxacvm0uLP8CVp5eT7U3lornFANy79iC+NA8zirLCSQaXTM1DRPjwytk0tjupMi6ZXzroeUM1hxlFmVy/bApBhYc2O8HnrGn5vP70cn5xw3I8AvetOzisa5voYo3bvDbGPgXuH+GyJF1ndxBfmtUczPgzoyiL9BQPQdURnfgV6msoG+aaD7+98Rw8ER3ZIsLnrlzAu86ZHh5lVJ7nC8+3WDLVWftiXmkOrx5sZL5bI7r2zApufWQnM4oyKYxjMl22N5X5ZdlcvqgsvETr3zfXku1NDc8LqcjLYNWCUv60rprPXjG/1wRAY6OVerGagxmv0lI8zC7Jcr5de0ZuLa5Qs9Jwaw4D/T31HYl16YISdtQ2s9DtuP7UZXO5bmllOFGgNzWFP3zkvCF9efv7p1eGA1NGWgrN/gDLZxT0+vm86+xp3OSmM3/daWVDuraJLq4ZXyLyRuB0IPz1QVW/lahCJYvfOqTNOHblaWXUNI7sCJyL5hbzvvNnsHxGweBvPgWXLSzltjV7Weh+yy/N9fWadAcwtzQ72qEDipwjMr88h02HGsOjpiLPW5rj5d5XDlpw6GPQ4CAivwAygUuBO4G3Aa8kuFyjTlWtQ9qMa5+7csGIf2ZeZhrfetPiEf/cvpbPKODWty3hqsWJmXuwyA0Oi/oEh9QUD29fMZWfr9lLbVNHXLmsJot47oQXqOr7gAZV/SZwPjAtscUafaFhgDYJzpjRJyK8Y8W0cIbVkRaqMSwo75/55w2LKwgqbDjQmJBzj1fxNCuF6qnt7joOdcCsxBUpOTq7neBgzUrGTDzXnVlJfVsXZ03P77cv1EE90AJGk1U8weEhEckHvg9swBmp9MtEFioZ/AFnGr/NkDZm4inK9vL5AZrd8jPTSE/1WHDoI2ZwEBEP8KSqNgJ/EZGHAZ+qNsU6bjwKLYRuNQdjJhdn7QkvRyw49BLza7KqBoEfRrzunIiBAZzZ0YDNczBmEirP9VnNoY947oSPichbJVZKxgmg021W8tkqcMZMOqW5Po4meEnT8SaePofPAVlAQET8OIv9qKpOqAV//NYhbcykVZ7r46kdx1DVmKnJJ5NBg4Oq5gz2nongZJ+DNSsZM9mU5/ro6O6h2R+wdaddg94JReTJeLaNd9YhbczkVeauWnes2U93T5D/fvw1rv3pc/z0yd0Eg5rk0iXHgDUHEfHhzIwuFpECnOYkcNaPrhyFso0qf8A6pI2ZrMrcBIO7j7Vyy9+28sr+euaVZvPDx1/j9Cm5XLZw8qXWiHUn/CiwHljo/ht6PAD8LPFFG12hmoPXOqSNmXTK3ZrDx+/ewKsHG/jRO5fy4CcvIsUjk3bmdKysrD8Gfiwin1LVn45imZKi05qVjJm0Ile6++2N53DBHGeNiUUVOWw42JCsYiVVPKOVgiKS706Ew21iereq3pbQko0ym+dgzOTlS0vhuS9dSlGWl4z0k18Qz5pWwP0bqukJKikjmAp9PIjnTviRUGAAUNUG4COnclIRebuIbBORoIisiNj+HhHZGPEIisjSUzlXvDoD1qxkzGQ2tSCzV2AAWDYjn7auHi7/4Rp+tnpPkkqWHPEEB0/kBDgRSQEGX4optq3A9cAzkRtV9W5VXaqqS4EbgCpV3XiK54qLvzuIRyAtZXJ9OzDGDGzZdGcdi6q6du56oYqeSTRyKZ7g8CjwRxG5XEQuA+4BHjmVk6rqDlXdNcjb3u2ea1SEFvqxCTDGmJAZRVn89sZz+Ma1p3G8pZPn95ygsb0r2cUaFaIaOxK6yfc+ClyOM5z1MeBOVe055ZOLrAG+oKrrouzbC7xJVbcOcOxNwE0AZWVly++9995hl6O1tZX7D6Sx9kiAn16eNezPGS9aW1vJzh7aqlrjnV3z5JCoa+7sUT79VDvdQfCmwK0XZ5KTPja+SJ7KNV966aXrVXVFtH3xzJAOishdwFNxfNsPE5EngGjLOt2iqg8Mcuy5QPtAgcEt1x3AHQArVqzQVatWxVu0ftasWUNhaQE5zSc4lc8ZL9asWTMprjOSXfPkkMhr/mDXTl7Ye4LN1U3s80zl5lXzEnKeoUrUNcezTOh1OGs5pAOz3A7ib6nqdbGOU9UrTqFc72IUm5TA1o82xsT25TcsBOBDd63lty9WcdPFs/t1YE8k8fQ5fB04B2gEcDuIZyaqQG4z1tuB4bcTDYO/O2hLhBpjBnXD+TOob+vi5f11yS5KQsUTHAIjvYaDiLxFRKpx1qP+u4g8GrH7YqBaVfeN5DkH0xnosTkOxphBrZhZiEdgw4GJPTkunklwW0XkX4AUEZkHfBp44VROqqp/Bf46wL41wHmn8vnD0dkdtLUcjDGDyvamsqgil3UTPDjE81X5U8DpQCdOP0Az8JkElikp/FZzMMbEacWMAjYeaiTQE0x2URJm0Luhqrar6i04Q1kvVdVbVHXCrafX3mUd0saY+CybUUB7Vw87j7QkuygJE896DmeLyBZgM7BFRDaJyPLEF210HW/ppDjbm+xiGGPGgRUzCwFYV1Wf5JIkTjztKL8CPq6qM1V1JvAJ4DcJLdUo6+pRmjq6w2l7jTEmlso8H+W5PtYfbEx2URImnuDQoqrPhl6o6nPAhKpLNXY6s8Qj0/YaY8xARITlMwtYP8lrDq+IyO0iskpELhGR24A1IrJMRJYluoCjocEfCg7WrGSMic+KGQUcbvJzuLEj2UVJiHiGsi51//16n+0XAApcNpIFSoZQcCi3moMxJk7LZzgZW9cfaKAyPyPJpRl58eRWunQ0CpJMDaFmJetzMMbEaVFFLpnpKbyw9wQXzi2mtqmD0yvzkl2sETNgs5KIXCsiMyJef80dqfSgiMwaneKNjkZ/kMz0FHK88VSkjDEG0lI8XLGojH9uPcJn7tvIW3/+Ai3+7mQXa8TE6nP4DnAcQESuAd4L3Ag8CPwi8UUbPQ2dSlmuz9ZyMMYMyVvOmkJjezfPvHYcf3eQx7YdTXaRRkys4KCq2u4+vx74laquV9U7gZLEF230NPjVOqONMUN20bxiCrPSyfGmUpnn428ba5JdpBETqx1FRCQbaMeZHX1bxL4J1Tjf2Kmcbp3RxpghSkvx8F/XnwHAluombluzh7rWToomwITaWDWHHwEbgXXAjtBqbSJyFlCb8JKNElV1mpWsM9oYMwyvP72c159ezqULSwgqEyYh34DBQVV/DVwCfAi4OmLXEeCDCS7XqDnR2kUgCJV5E28omjFm9JxemUd6iof1EyQ4xByeo6o1QE2fbROm1gBQ3eB0q0wrtOBgjBk+X1oKZ0zNY21VPT98bBcbDzVy5enl3HDejMEPHoMm/djNQw3O7MapBZlJLokxZrxbMaOA25/Zx6sHGynN8fLi3jrOmVnIgvKcZBdtyCb9AgahmsOUCTjD0Rgzupa5s6YvnFvEP29eSY4vlW89vC3JpRqemMFBRDwisnW0CpMM1Q0d5KRDlk2AM8acopXzivmXc6fzvbcuoSjby/XLprLhQCOqmuyiDVnM4KCqQWCTiEwfpfKMukP17RRnTPoKlDFmBGSmp/Kfbzkj3Ew9tSCDju4e6tu6klyyoYvn63IFsE1EXgHaQhtV9bqElWoU1TR0UJxhM6ONMSMvFCRqGjvG3dyHeILDNxNeiiQJBpXqxg4WTrPlQY0xIy/Ul1nd0MGSqfnJLcwQxZOV9Wk3Ad88VX1CRDKBCXE3PdHaSVcgSHGG9TcYY0belIJQcGgf5J1jTzxrSH8E+DNwu7tpCvC3BJZp1Bxyf2HWrGSMSYS8jDRyfKnUNIy/BYHi6Yn9BHAh0AygqruB0kQWarScMSWfJz9/CfMKJkRFyBgzBk0tyKR6ggaHTlUNd7WLSCrOCnDjXnqqhzkl2WSkWs3BGJMYU/IzqGnsIBhUHtt2ZNys+RBPcHhaRP4NyBCR1wF/Ah5KbLGMMWZimFqQwcH6dt7/m1e46ffr+f1LB5JdpLjEExy+jLPozxbgo8A/gK8mslDGGDNRzCnJor2rh3VVDfjSPOw/3jb4QWNAPMN0VgF3q+ovE1wWY4yZcN5x9jQWT8ljUUUuN/zqZQ7UjY+RS/HUHD4AbBSRF0XkVndt6YIEl8sYYyYEb2oKZ00vwJeWwoyiLA7Uj4+aw6DBQVXfp6rzgbcC1cDPcNeWNsYYE7+ZRZkcbe6ko6sn2UUZ1KDNSiLyXmAlcAZwAvhf4NkEl8sYYyac6UVZABysbx/zabzj6XP4EbAX+AWwWlWrElkgY4yZqGYWObmWquraxnxwiKdZqRi4EfAB3xGRV0Tk96dyUhH5DxHZLCIbReQxEal0t7/H3RZ6BEVk6amcyxhjxooZhU7N4UDd2O93iCd9Ri4wHZgBzATygOApnvf7qrpEVZcCDwNfA1DVu1V1qbv9BqBKVTee4rmMMWZMyMtMIz8zjT3HWpNdlEHFM1rpOeBaYDPwTlVdoKrvP5WTqmpzxMssos+4fjdwz6mcxxhjxppLF5Ty8OZaGtvH9hoPEu8KRSKSA6iqjkjIE5HvAO8DmoBLVfV4n/17gTepatSV6ETkJuAmgLKysuX33nvvsMvS2tpKdnb2sI8fbybb9YJd82QxHq65uiXIV5/v4M1z03jz3PRT/rxTueZLL710vaquiLpTVWM+gMXAq8AB4CCwHlgcx3FPAFujPN7U531fAb7ZZ9u5wJbBzhF6LF++XE/F6tWrT+n48WayXa+qXfNkMV6u+UN3vaJnfvNRbfV3n/Jnnco1A+t0gPtqPM1KdwCfU9UZqjod+Ly7LSZVvUJVF0d5PNDnrX/AmUMR6V1Yk5IxZoL6f6vm0tjezT2vHEx2UQYUT3DIUtXVoRequgann2DYRGRexMvrgJ0R+zzA24HhtxMZY8wYtnxGAefOKuTOZ/eHWkvGnHiCwz4R+XcRmek+vgrsP8XzfldEtorIZuBK4OaIfRcD1aq67xTPYYwxY9brTy/nSLOfxvbBU3j/7sUqHthYMwqlOimeSXA34qwjfb/7+hngg6dyUlXt24wUuW8NcN6pfL4xxox1pbleAI62+CnIGrhjurqhnW89tJ3yPB/XnVmJyOisPzNgcBARH/AxYC5Ouu7Pq+r4WKXCGGPGuLJcHwDHmjtZWD7w+25/eh+BoFLd0MG+E23MKRmd0VixmpV+C6zACQxvAL4/KiUyxphJoDTHrTk0+6Pu7woE+dx9G/n9Swe4ZH4JAE/vGr2cp7GCw2mq+l5VvR14G05fgDHGmBFQmuPWHFo6o+5/YsdR7n+1hg9fNItfvHc5s0uyePq1sREcwk1IqhoYhbIYY8ykkZGeQo4vlWMD1Bwe2FhDSY6Xr1y9iIz0FC6eV8LL++voCpxq9qL4xAoOZ4pIs/toAZaEnotIc4zjjDHGxKE0xxu15tDs72b1ruNcs6SCFI/TAX3urEL83UG21DSNStkGDA6qmqKque4jR1VTI57njkrpjDFmAivL9bHrSAsrvv0ED246HN6+eucxugJBrj2zMrzt7FmFAKytqh+VssUzlNUYY0wClOZ4eWFvHQDffng7T+86Tnmel8b2bnK8qZw5NT/83uJsL7NLsnhlfz0fu2ROwstmwcEYY5Kk1B3OmpmewrGWTv6yoRpfmofKvAyWTs8PNymFnDurkIc319IT1H77Rlo8M6SNMcYkQGg469VnVPCDt5/J5143H393kH0n2lgxo7Df+8+cmk+LP0BNQ0fCy2bBwRhjkiQ0EW7VghLetnwqH7tkDtlep0Fn+YyCfu+fWuAsM3q4yYKDMcZMWKsWlPCZK+ZxxaIyANJTPVyyoASPwNLp+f3eX5nvBJPDjYkPDtbnYIwxSZLjS+MzV8zvte1Lr1/IdWdWhmsQkSryMgCobYo+N2IkWXAwxpgxZHpRJtOLMqPuy0hPoTArnZpRqDlYs5Ixxowjlfm+UWlWsuBgjDHjSGVeBjUNHfzHw9vZdjhxs6WtWckYY8aRyvwMHtt+lN3HWklNEc7PSMx5rOZgjDHjSGjEEkB1feKalyw4GGPMOFKZf7KqcKihPWHnseBgjDHjyPyyHABmF2dxqN6CgzHGGJzgsPaWK3jH2dNoaO+mI6AJOY8FB2OMGWdKcrxMc1NpHG9PzOI/FhyMMWYcmlbo9D0c77CagzHGGFeo5nDCgoMxxpiQ/Mw0sr2p1qxkjDHmJBFhRlEm/p7EfL4FB2OMGace+uRFfPgMb0I+24KDMcaMU54ELhVqwcEYY0w/FhyMMcb0Y8HBGGNMPxYcjDHG9GPBwRhjTD9JCQ4i8g0RqRGRje7janf7eyK2bRSRoIgsTUYZjTFmMkvmSnD/o6o/iNygqncDdwOIyBnAA6q6MQllM8aYSW0sNyu9G7gn2YUwxpjJKJk1h0+KyPuAdcDnVbWhz/53Am8a6GARuQm4yX3ZKiK7TqEsxcCJUzh+vJls1wt2zZOFXfPQzBhoh6gmJqOfiDwBlEfZdQvwEs7FKPAfQIWq3hhx7LnAnap6RkIK17+s61R1xWicayyYbNcLds2ThV3zyElYzUFVr4jnfSLyS+DhPpvfhTUpGWNM0iRrtFJFxMu3AFsj9nmAtwP3jna5jDHGOJLV53CrO0RVgSrgoxH7LgaqVXXfKJbnjlE811gw2a4X7JonC7vmEZKwPgdjjDHj11geymqMMSZJLDgYY4zpZ1IHBxG5SkR2icgeEflyssszGkSkSkS2uOlJ1iW7PIkgIr8WkWMiEjnQoVBEHheR3e6/Bcks40gb4JqjpqmZCERkmoisFpEdIrJNRG52t0/Y33OMa07I73nS9jmISArwGvA6oBpYC7xbVbcntWAJJiJVwApVnbAThUTkYqAV+J2qLna33QrUq+p33S8CBar6pWSWcyQNcM3fAFr7pqmZCNwRjxWqukFEcoD1wJuBDzBBf88xrvkdJOD3PJlrDucAe1R1n6p24QydHXBGthk/VPUZoL7P5jcBv3Wf/xbnj2rCGOCaJyxVrVXVDe7zFmAHMIUJ/HuOcc0JMZmDwxTgUMTrahL4gx5DFHhMRNa7KUgmizJVrQXnjwwoTXJ5RssnRWSz2+w0YZpYIonITOAs4GUmye+5zzVDAn7Pkzk4RFuZezK0sV2oqsuANwCfcJsjzMT0c2AOsBSoBX6Y1NIkgIhkA38BPqOqzckuz2iIcs0J+T1P5uBQDUyLeD0VOJyksowaVT3s/nsM+CtO89pkcDQ0M9/991iSy5NwqnpUVXtUNQj8kgn2uxaRNJyb5N2qer+7eUL/nqNdc6J+z5M5OKwF5onILBFJx8nn9GCSy5RQIpLldmQhIlnAlUSkLpngHgTe7z5/P/BAEssyKmKlqRnvRESAXwE7VPW/I3ZN2N/zQNecqN/zpB2tBOAO+foRkAL8WlW/k9wSJZaIzMapLYCTOuUPE/GaReQeYBVOKuOjwNeBvwF/BKYDB4G3q+qE6cAd4JpX4TQ1hNPUhNrjxzsRuQh4FtgCBN3N/4bTBj8hf88xrvndJOD3PKmDgzHGmOgmc7OSMcaYAVhwMMYY048FB2OMMf1YcDDGGNOPBQdjjDH9JGslOGPGBBHpwRkaGPJmVa1KUnGMGTNsKKuZ1ESkVVWzB9gnOH8jwWj7jZnIrFnJmAgiMtPNl38bsAGYJiL/KiJr3cRm34x47y3ueiBPiMg9IvIFd/saEVnhPi9206QjIiki8v2Iz/qou32Ve8yfRWSniNztBiZE5GwReUFENonIKyKSIyLPirMGe6gcz4vIktH6GZnJwZqVzGSXISIb3ef7gc8CC4APqurHReRKYB5OvhoBHnSTFbbhpFw5C+fvaANOfv1YPgQ0qerZIuIFnheRx9x9ZwGn4+T3eh64UEReAe4D3qmqa0UkF+gA7sRZt+AzIjIf8Krq5lP8ORjTiwUHM9l1qOrS0As3FfIBVX3J3XSl+3jVfZ2NEyxygL+qart7XDx5ua4ElojI29zXee5ndQGvqGq1+1kbgZlAE1CrqmsBQllHReRPwL+LyL8CNwJ3DfGajRmUBQdj+muLeC7Af6nq7ZFvEJHPMHCK9wAnm2x9fT7rU6r6aJ/PWgV0RmzqwfnblGjnUNV2EXkcZ2GbdwArYl6NMcNgfQ7GxPYocKObQx8RmSIipcAzwFtEJMPNdHttxDFVwHL3+dv6fNb/c9MuIyLz3ey4A9kJVIrI2e77c0Qk9IXuTuAnwNqJkljOjC1WczAmBlV9TEQWAS+6fcStwHvddXzvAzYCB3CyZYb8APijiNwAPBWx/U6c5qINbofzcWIsY6mqXSLyTuCnIpKB099wBc56wetFpBn4zYhcqDF92FBWY0aAiHyDBCzyHuN8lcAaYKENtTWJYM1KxowzIvI+nHULbrHAYBLFag7GGGP6sZqDMcaYfiw4GGOM6ceCgzHGmH4sOBhjjOnHgoMxxph+/j+EV027tc/gGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_fs = 50\n",
    "ppg_ds = signal.decimate(ppg_filt, round(fs/ds_fs))\n",
    "Pxx, freqs = plt.psd(ppg_ds, NFFT=512, Fs=ds_fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4db0809",
   "metadata": {},
   "source": [
    "Note that this could also be done using scipy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "1d019c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, Pxx_spec = signal.welch(ppg_ds, ds_fs, nperseg=512)\n",
    "plt.semilogy(f, Pxx_spec)\n",
    "plt.xlabel('frequency [Hz]')\n",
    "plt.ylabel('PSD [V**2/Hz]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f39f17ef",
   "metadata": {},
   "source": [
    "Most of the frequency content is below 7.5 Hz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "831d2c73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99% of the frequency content is below 7.32421875 Hz\n"
     ]
    }
   ],
   "source": [
    "total_Pxx_spec = sum(Pxx_spec)\n",
    "freq_no = 0\n",
    "curr_sum = 0\n",
    "while curr_sum < 0.99*total_Pxx_spec:\n",
    "    curr_sum = curr_sum+Pxx_spec[freq_no]\n",
    "    freq_no += 1\n",
    "\n",
    "print('99% of the frequency content is below {} Hz'.format(f[freq_no]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b553c9",
   "metadata": {},
   "source": [
    "- Let's repeat for the ECG:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "9df88945",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecg_filt = signal.sosfiltfilt(sos_ppg, ecg)\n",
    "ds_fs = 100\n",
    "ecg_ds = signal.decimate(ecg_filt, round(fs/ds_fs))\n",
    "Pxx, freqs = plt.psd(ecg_ds, NFFT=1024, Fs=ds_fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a30cba5",
   "metadata": {},
   "source": [
    "For the ECG, most of the frequency content is below 35 Hz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "5a263bba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99% of the frequency content is below 33.984375 Hz\n"
     ]
    }
   ],
   "source": [
    "f, Pxx_spec = signal.welch(ecg_ds, ds_fs, nperseg=1024)\n",
    "total_Pxx_spec = sum(Pxx_spec)\n",
    "freq_no = 0\n",
    "curr_sum = 0\n",
    "while curr_sum < 0.99*total_Pxx_spec:\n",
    "    curr_sum = curr_sum+Pxx_spec[freq_no]\n",
    "    freq_no += 1\n",
    "\n",
    "print('99% of the frequency content is below {} Hz'.format(f[freq_no]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd423e5f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "data-exploration.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "306px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
